Correlation of Performance, Exhaust Gas Temperature and Speed of a Spark Ignition Engine Using Kiva4

The objective of this study was to investigate performance characteristics of a spark ignition engine, particularly, the correlation between performance, exhaust gas temperature and speed, using Kiva4. Test data to validate kiva4 simulation results were conducted on a 3-cylinder, four-stroke Volkswagen (VW) Polo 6 TSI 1.2 gasoline engine. Three different tests were, therefore, carried out. In one set, variations in exhaust gas temperature were studied by varying the engine load, while keeping the engine speed constant. In another test, exhaust gas temperature variations were studied by keeping the engine at idling whilst varying the speeds. A third test involved studying variations in exhaust gas temperature under a constant load with variable engine speeds. To study variations in exhaust gas temperatures under test conditions, a basic grid/mesh generator, K3PREP, was employed to write an itape17 file comprising of a 45° asymmetrical mesh. This was based on the symmetry of the combustion chamber of the engine used in carrying out experimental tests. Simulations were therefore performed based on the input parameters established in the conducted tests. Simulations with the kiva4 code showed a significant predictability of the performance characteristics of the engine. This was evident in the appreciable agreement obtained in the simulation results when compared with the test data, under the considered test conditions. A percentage error, between experimental results and results from simulations with the kiva4 code of only between 2% to 3% was observed.

Study and Redesign of Aircooler System in a 16 MW Steam Turbine Surface Condenser at Neka Power Plant

According to the study of basic Rankin thermal cycle, the steam exhaust pressure of a typical steam turbine toward the condenser, plays a great role in the efficiency and the net output power of the steam turbine, so most surface condensers that are working in thermal power plants are kept at vacuum condition so that the maximum power of thermal cycle can be achieved. The vacuum pressure at condenser leads to the entering of air and Non-condensable gases from turbine gland seals to condenser so that the special air ejection equipment is being used to take apart air from steam and vent it to out of condenser. In this study, a special steam and air separator mechanism in an evacuating system called “Aircooler” at a 16 MW steam turbine condenser is being studied and the Fluent CFD software is utilized to analyze the behavior of steam plus air in a typical aircooler system of 16 MW steam turbine condenser of Neka power plant to find a way to reduce the risk of cooling tube rupture in aircooler ducts. The critical condition which tube rupture happens is determined and it is demonstrated that in hot seasons of year, by increasing the seawater cooling temperature and increasing in turbine steam exhaust pressure and temperature, the risk of tube rupture due to more mixture velocity at the first row of aircooler cooling tubes increases and also the effect of tube plugged condition on the performance of aircooler shows that the risk of other tubes rupture will increase and thus the efficiency of aircooler decreases due to more aircooler exhaust temperature. Finally, two modified plans at aircooler system design will be studied and simulated via Fluent CFD software which leads to reduce the risk of tube rupture. The results show that by modification of aircooler ducts and holes, the mixture air and steam flow velocity to first aircooler cooling tube row decreases significantly and causes the risk of tube rupture to decrease remarkably and also the exhaust temperature of aircooler decreases and causes the higher ejector performance.

Remaining Useful Life Prediction for Aero-Engines Combining Sate Space Model and KF Algorithm

The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.

A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines

The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.

High NO_2/NO_X emissions downstream of the catalytic diesel particulate filter:An influencing factor study

Diesel vehicles are responsible for most of the traffic-related nitrogen oxide（NO x） emissions,including nitric oxide（NO） and nitrogen dioxide（NO2）. The use of after-treatment devices increases the risk of high NO2/NO x emissions from diesel engines. In order to investigate the factors influencing NO2/NO x emissions, an emission experiment was carried out on a high pressure common-rail, turbocharged diesel engine with a catalytic diesel particulate filter（CDPF）. NO2 was measured by a non-dispersive ultraviolet analyzer with raw exhaust sampling. The experimental results show that the NO2/NO x ratios downstream of the CDPF range around 20%–83%, which are significantly higher than those upstream of the CDPF. The exhaust temperature is a decisive factor influencing the NO2/NO x emissions. The maximum NO2/NO x emission appears at the exhaust temperature of 350°C. The space velocity,engine-out PM/NO x ratio（mass based） and CO conversion ratio are secondary factors. At a constant exhaust temperature, the NO2/NO x emissions decreased with increasing space velocity and engine-out PM/NO x ratio. When the CO conversion ratios range from 80% to 90%,the NO2/NO x emissions remain at a high level.